Carbon Balance and Management
BioMed Central
Open Access
Review
Africa and the global carbon cycle
Christopher A Williams*1, Niall P Hanan1, Jason C Neff2, Robert J Scholes3,
Joseph A Berry4, A Scott Denning5 and David F Baker6
Address: 1Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA, 2University of Colorado, Boulder, CO
80309, USA, 3Council for Scientific and Industrial Research, Pretoria 001, South Africa, 4Carnegie Institution of Washington, Stanford, CA, 94305,
USA, 5Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO 80523, USA and 6National Center for Atmospheric
Research, Terrestrial Science Section, Climate and Global Dynamics Division, 1850 Table Mesa Dr., Boulder, CO 80307, USA
Email: Christopher A Williams* - [email protected]; Niall P Hanan - [email protected]; Jason C Neff - [email protected];
Robert J Scholes - [email protected]; Joseph A Berry - [email protected]; A Scott Denning - [email protected];
David F Baker - [email protected]
* Corresponding author
Published: 7 March 2007
Carbon Balance and Management 2007, 2:3
doi:10.1186/1750-0680-2-3
Received: 19 January 2007
Accepted: 7 March 2007
This article is available from: http://www.cbmjournal.com/content/2/1/3
© 2007 Williams et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The African continent has a large and growing role in the global carbon cycle, with potentially
important climate change implications. However, the sparse observation network in and around
the African continent means that Africa is one of the weakest links in our understanding of the
global carbon cycle. Here, we combine data from regional and global inventories as well as forward
and inverse model analyses to appraise what is known about Africa's continental-scale carbon
dynamics. With low fossil emissions and productivity that largely compensates respiration, land
conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna
fire emissions, though large, represent a short-term source that is offset by ensuing regrowth.
While current data suggest a near zero decadal-scale carbon balance, interannual climate
fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and
savanna fire emissions such that Africa is a major source of interannual variability in global
atmospheric CO2. Considering the continent's sizeable carbon stocks, their seemingly high
vulnerability to anticipated climate and land use change, as well as growing populations and
industrialization, Africa's carbon emissions and their interannual variability are likely to undergo
substantial increases through the 21st century.
Background
Africa stands out among continents for widespread and
deeply entrenched poverty, slow economic development,
and agricultural systems prone to failure during frequent
and persistent droughts [1]. Africa is also home to some
rapidly developing economies, tremendous natural
resources and remarkable social and ecological diversity.
The unique history of Africa, the close dependencies of
people on natural resources and a future that will certainly
include substantial industrial, agricultural and social
development, suggest that Africa will become a key player
in the carbon cycle of the 21st century. However, our
knowledge about Africa's current role in the global carbon
cycle remains remarkably limited. We currently do not
know whether Africa is a net sink or source of atmospheric
carbon, and have only vague indications of the continent's temporal and spatial patterns of carbon exchange.
Given the current development agenda that is intended to
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Carbon Balance and Management 2007, 2:3
elevate Africa's importance in the global economy [2], it is
time to focus as well on Africa's role in the global carbon
cycle. Here we review what is known about Africa's carbon
dynamics from regional and global inventories, and forward and inverse model analyses, and highlight some of
the unique features of Africa's contribution to global carbon fluxes.
http://www.cbmjournal.com/content/2/1/3
of the African carbon cycle impacts our ability to assess
global carbon dynamics. We then outline where new
measurements and further research are most likely to contribute understanding of African and global carbon
cycling, and discuss implications for African involvement
in international climate change agreements.
Africa in the Balance
The diverse elements of the global carbon cycle have been
the focus of much recent research [3-5]; research that is
vital to our understanding of the missing carbon sink,
future atmospheric carbon dioxide concentrations, and
future climate [6-8]. Much of that research has concentrated on carbon dynamics of the large ocean basins
[9,10] and terrestrial exchange in North America [11,12]
and Eurasia [13,14]. Despite representing 20% of the global land mass, Africa has thus far been largely neglected in
these studies. Africa contributes a disproportionately
small fraction of the global fossil fuel carbon emissions
that are responsible for rising atmospheric carbon dioxide
concentrations, with 14% of global population [15], but
only 3% of fossil emissions [16]. In contrast, Africa plays
a globally important role in fire and land use carbon emissions, though the magnitudes of these terms are highly
uncertain.
To date, continental assessments of Africa's carbon
dynamics are primarily model-based. Plausible estimates
of Africa's regional sources and sinks can potentially be
supplied by atmospheric inversion using global CO2 concentration measurements and atmospheric transport
models. However such 'top-down' solutions have large
uncertainties, particularly for Africa and other tropical
regions, due to the paucity of appropriately located CO2
concentration measurements [17,18]. Existing data are
also insufficient to partition carbon sources and sinks
within Africa, and inversion techniques provide little
insight regarding mechanisms responsible for net uptake
and release of carbon in space and time [17,19,20]. The
alternative approach is to perform spatially, temporally
and source-differentiated 'bottom-up' estimation using
biogeochemical models. However, such regional carbon
flux estimates are only weakly supported by the sparse
network of place-based observations, and thus are largely
founded on models that have been parameterized and
evaluated with extra-African observations. The resultant
uncertainty reduces our ability to resolve African and global carbon sources and sinks, and hinders wise resource
management in Africa for greenhouse gas mitigation.
With the hope of identifying carbon cycle research priorities that may be met through focused research efforts, we
synthesize current understanding of carbon stocks and
fluxes within Africa, highlight uncertainties in those
terms, and diagnose where uncertainty in our knowledge
Africa is second only to Eurasia in continental surface
area. It has large areas of moist tropical forest, seasonal
and semi-arid woodland, savanna, grassland and desert,
as well as smaller regions of Mediterranean and montane
vegetation in extra-tropical and high elevation areas (Figure 1).
Initial estimates [21] of carbon stocks and the various flux
pathways (Table 1, Figure 2) suggest that the continent
plays a significant role in atmospheric CO2 dynamics at
time scales ranging from sub-seasonal to decadal and
longer. The balance of terms in Figure 2 should not be
interpreted as identifying a large net biotic source for the
continent but rather that independent studies which estimate the magnitude of fluxes associated with individual
pathways can not be used in a budget calculation without
careful consideration of the processes represented in each
estimate and the associated uncertainties. For example,
biomass burning emissions are not modeled explicitly in
many biogeochemical or biophysical models and may
thus be effectively lumped into heterotrophic respiration.
Patterns of soil and vegetation carbon stocks and net primary production (NPP) are highly correlated with annual
rainfall (Figure 1). Africa's fraction of global annual NPP
is estimated to be similar to the fractional terrestrial area
of the continent (Table 1 and Figure 1); the large unproductive arid regions are compensated by high productivity
in forests and woodlands. Carbon stocks and NPP per unit
land area center on the equator and decline to the north
and south toward increasingly arid environments. However, greater land area in Africa's northern hemisphere
cause latitudinally summed C stocks and NPP to peak
north of the equator (Figure 1).
African fossil fuel emissions are a tiny fraction of global
totals, even when normalized by land area or human population (Table 1), while renewable energy sources (wood,
charcoal) are a substantial component of domestic emissions. With low fossil emissions, Africa's current continental scale carbon fluxes are dominated by biogenic
uptake and release from terrestrial ecosystems as well as
pyrogenic emissions in savanna and forest fires. As is generally true globally, the continent's large carbon uptake
from photosynthesis is offset by an equivalently large respiration flux, leading to near-zero net biotic flux at multiyear or longer timescales. In spite of these broad patterns,
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40
30
Live
Soil
Latitude [degrees]
20
Potsdam17
CASA
Live
Soil
Potsdam17
CASA
10
0
-10
-20
-30
-40
-10
0
40
10
30
20
Longitude [degrees]
50
0
500 1000 1500 0
Mean Annual Precipitation
[mm yr-1]
3
6
9
C Density
[kg m-2]
12 0 200 400 600 800
Annual NPP
[g C m-2 y-1]
0
10
20 30
Total C
[Pg]
40 0 0.5 1.0 1.5 2.0 2.5
Annual NPP
[Pg C y-1]
urban
bareground
cropland
open and closed shrublands
wooded and nonwooded grasslands
woodland
mixed forest
deciduous needleaf and broadleaf forests
evergreen needleleaf and broadleaf forests
Latitudinal
(NPP)
total annual
Figure
per
1 unit
distribution
NPP,
ground
shown
of
area
with
mean
from
the
annual
CASA
spatial
precipitation
[23,
distribution
26] and[72],
the
of land
soil
Potsdam
cover
[78] and
17-model
[106]
plant
(colors)
[84]
intercomparison
carbon density,
[89],
annual
totalnet
soil
primary
and live
production
carbon,
Latitudinal distribution of mean annual precipitation [72], soil [78] and plant [84] carbon density, annual net primary production
(NPP) per unit ground area from CASA [23, 26] and the Potsdam 17-model intercomparison [89], total soil and live carbon,
total annual NPP, shown with the spatial distribution of land cover [106] (colors). Means and totals were calculated from published data using all terrestrial locations in 5° latitude zones.
estimates can differ widely between studies (Table 1) and
temporal variability is large.
Bottom-up simulation models [22-24] indicate large
interannual variation in Africa's net ecosystem carbon
exchange (NEE), with an interannual variability
(expressed as the standard deviation of annual NPP) that
is approximately 50% of the variability estimated for the
global land mass (Figure 3), primarily induced by climate
fluctuations [24]. Particularly large between-year coefficients of variation in NPP are found for Africa's woodlands, savannas, and grasslands, according to one model
incorporating satellite measurements of vegetation activity [25,26].
Africa plays a global role in C emissions through land use
and fire (Table 1), though lack of information from the
limited number of studies on the continent [e.g. [27-31]]
restricts confidence in their magnitudes. Deforestation is
the largest term in current assessments of tropical land use
emissions [32], with Africa contributing 25% to 35% of
total tropical land clearing from deforestation, and as
much as 0.37 Pg C y-1, in the last decades [32,33]. Carbon
losses through deforestation tend to be 'permanent' in
Africa, as afforestation and reforestation rates are modest,
at less than 5% of annual deforestation [32]. The associated net release of carbon from land use in sub-Saharan
Africa is estimated to be 0.4 Pg C y-1, or 20% of the tropical total, nearly all attributed to deforestation [32].
Annual net C emissions from conversion to agriculture
and cultivation practices alone are estimated [24] to be
about 0.8 Pg C y-1 for tropical land masses, but only 0.1 Pg
C y-1 from Africa [24,31], where shifting cultivation is
prevalent [31].
Lack of information prohibits even the best land use
change C emissions assessments from including all of the
terms anticipated to be important for Africa. Pastoralism,
shifting cultivation, and domestic wood harvest are widespread across the continent, but are often assumed to be
inconsequential or are not considered [e.g. [32]], such
that land use and land use change emissions from Africa
are likely to be underestimated. Recent work [31] explicitly simulates aspects of these practices, though still
focuses exclusively on forest and cropland conversions,
missing land use change C emissions in Africa's vast
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The African Carbon Cycle
NPP
Precipitation
-0.04
-10
11
Fossil & Cement
0.2
Plants
(80)
Rh
Biomass
Burning
1.1
0.4
Rivers
Land Use
0.05
Ocean
Soils
(200)
Figure
The
African
2 carbon cycle
The African carbon cycle. Annual fluxes and pools (shown in parentheses) all in units of 1015 g C, where NPP is net primary
production, and Rh is heterotrophic respiration. Estimates as reported in Table 1
savannas and grasslands which are home to much of the
continent's livestock and the center of Africa's cereal and
grain production. Furthermore, net C fluxes associated
with changes in land use practices but not involving land
conversion, such as management of tillage, slash, crop residues, and crop rotation, are refinements currently missing from continental scale land use change assessments.
Finally, much of Africa, particularly in the semi-arid
regions, is vulnerable to degradation, that may be the
result of periodic drought or caused by agricultural and
pastoral activities, releasing presumably large but
unknown amounts of CO2 from cleared and dead vegetation [34] as well as possibly triggering strong biophysical
feedbacks to the climate system [35] that may accelerate
warming and prolong droughts [36-38].
Fire and land use emissions of carbon are entwined, especially in the humid and subhumid forest areas where fire
is a primary tool for land transformation. Fire emissions
associated with deforestation, shifting cultivation, burning of agricultural residues, and fuelwood may be as large
as 2 Pg C y-1 globally and 0.4 Pg C y-1 for Africa, each of
similar magnitude to estimates of total land use-related C
emissions from those regions (Table 1). Consequently,
estimates of land use change and deforestation C emissions already include, at least in theory, the associated fire
emissions. New methods to estimate fire emissions using
satellite sensors and atmospheric carbon monoxide measurements [39,40] will improve our ability to diagnose C
emissions in fires.
Fire is also a common dry season occurrence in the seasonal savannas that encircle the humid forest zone. Carbon emissions in savanna fires represent a much shorterterm C loss than forest fires, since the main fuel is dead
herbaceous vegetation, representing just one or two years
of growth [27,41]. Thus savanna fires may only lead to
faster cycling of biomass carbon rather than a net emission. Even if carbon emissions from savanna fires are
roughly balanced over the long-term by growth in subsequent years, fires provide intense and localized injections
of carbon into the atmosphere potentially shifting the seasonal or interannual distribution of CO2 releases [27,41].
Given the large magnitude of these fluxes in Africa, even
fairly small (e.g. 20%) variation in year to year total fluxes
could translate into annual variation in pyrogenic fluxes
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Africa Total
Africa/Global
Global Citations
Africa Citations
148.8
6.38
1600 ± 220 (2060, 1395)
610 ± 47 (654, 559)
-56 ± 10 (-72, -37)
57 ± 17 (81, 34)
6.2 ± 0.2 (6.4, 6.0)
1.7 ± 0.71 (2.8, 0.8)
1.42 ± 0.64 (2.2, 0.5)
0.83 ± 0.17 (1.0, 0.6)
2.9 ± 0.9 (4.7, 1.5)
0.36 ± 0.26 (0.89, 0.13)
0.60 ± 0.30 (1.13, 0.34)
1.45 ± 1.14 (4.1, 0.22)
0.51 ± 0.36 (1.26, 0.28)
0.41 ± 0.24 (0.80, 0.13)
0.71 ± 0.13 (0.8, 0.62)
0.41 ± 0.03 (0.44, 0.38)
0.29 ± 0.16 (0.40, 0.18)
-0.51 ± 0.17 (0.68, 0.34)
-0.08 ± 0.02 (-0.10, -0.06)
-0.43 ± 0.15 (-0.58, -0.28)
0.33 ± 0.11 (0.40, 0.25)
0.02 ± 0.01 (0.03, 0.01)
0.30 ± 0.09 (0.36, 0.24)
0.27 ± 0.14 (0.51, 0.12)
0.15 (--)
0.023 (--)
-0.345 (--)
0.370 (--)
30.2
0.87
200 ± 50 (240, 170)
80 ± 28 (105, 50)
-10 ± 3 (-13, -7)
11 ± 5 (-18, -7)
0.2 (--)
0.36 ± 0.05 (0.4, 0.3)
0.24 ± 0.12 (0.37, 0.08)
0.10 ± 0.01 (0.11, 0.08)
1.1 ± 0.5 (1.8, 0.3)
0.07 (--)
0.24 (--)
1.47 ± 0.33 (1.67, 1.09)
0.16 ± 0.08 (0.24, 0.08)
0.01 (--)
0.055 ± 0.021 (0.07, 0.04)
0.040 ± 0.014 (0.05, 0.03)
0.017 ± 0.006 (0.021, 0.013)
-0.036 ± 0.025 (-0.054, -0.018)
-0.009 ± 0.006 (-0.013, -0.004)
-0.028 ± 0.019 (-0.041, -0.014)
0.007 (--)
0.005 ± 0.001 (0.007, 0.004)
0.07 (--)
0.09 ± 0.02 (0.11, 0.07)
0.05 ± 0.04 (0.08, 0.02)
0.038 (--)
-0.023 (--)
0.061 (--)
0.20
0.14
0.13
0.13
0.19
0.19
0.03
0.21
0.17
0.12
0.37
0.19
0.41
1.02
0.32
0.03
0.08
0.10
0.06
0.07
0.11
0.06
0.02
0.33
0.24
0.32
0.33
NA
0.07
0.16
-[15]
[77–81]
[77, 80, 82, 83]
[24-26, 80, 83, 86-89]
[24, 87, 90, 91]
[16, 77, 92]
[32, 77, 92, 93]
[29, 32, 33, 95, 96]
[24]
[27, 28, 40, 76, 97–100]
[27, 76, 97–100]
[27, 28, 76, 97–99]
[27, 28, 41, 76, 97–99]
[27, 28, 76, 97–99]
[27, 28, 76, 97–99]
[35, 70]
[35, 70]
[35, 70]
[71]
[71]
[71]
[77, 103]
[40, 41]
[77, 105]
[40] [27, 28]
[105]
[47]
[47]
[47]
-[15]
[22, 81]
[22, 84, 85]
[22, 24–26, 89]
[24, 91]
[16]
[31, 32, 94]
[32, 33]
[24, 31]
[40, 76, 98, 100, 101]
[76]
[76]
[41, 76, 98, 102]
[76]
[76]
This study
This study
This study
This study
This study
This study
This study
[40, 41]
[105]
[40]
[46, 105]
[47]
[47]
[47]
NA is not applicable, DIC is dissolved inorganic carbon, DOC is dissolved organic carbon, NMHC is non-methane hydrocarbon, biomass import and export are the sum of cereal, paper, and wood
products.
Shown are means and standard deviations of published estimates with maximum and minimum values in parentheses. Positive fluxes refer to a terrestrial source from Africa to the atmosphere, ocean, or
other land masses as appropriate. Carbon stocks and fluxes have units of Pg C and Pg C y-1, respectively. See Appendices 1 for methods of calculation. Inconsistencies among tabular values arise in part due
to inclusion of studies that report only some of the terms and between study variation in methods of estimation (e.g. through use of reports containing global but not African deforestation rates).
Page 5 of 13
Land Area [1012m2]
Human Population [109]
Soil Carbon
Live Plant Carbon
Net Primary Production
Heterotrophic Respiration
Fossil Emissions
Net Land Use Emissions
Deforestation
Conversion to Crops
Biomass Burning
Deforestation
Shifting Cultivation
Savanna Fires
Fuel wood
Agricultural Residues
Riverine C Discharge
DIC
DOC
Precipitation C Flux
DIC
DOC
CH4Emissions
CH4 from fires
CO Emissions
CO from fires
NMHC Emissions
Net Biomass Trade
Gross Import
Gross Export
Global Total
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Carbon Balance and Management 2007, 2:3
Table 1: Global terrestrial and African carbon stocks and fluxes representative of the 1990s.
Standard Deviation of NEE
[Pg C y-1]
Carbon Balance and Management 2007, 2:3
2.0
1.5
tion (Table 1). Africa is also a minor net global source of
biomass carbon through international exchange, mainly
from export of wood products [47].
Africa
Global Land
Africa / Global
0
What the Atmosphere Sees
45%
1.0
0.5
http://www.cbmjournal.com/content/2/1/3
50%
HRBM
50%
TEM
LPJ
Figure and
Standard
estimated
Biosphere
(TEM),
McGuire
3deviation
etwith
Model
Lund-Potsdam-Jena
al. [24]
three
(HRBM),
of ecosystem
net ecosystem
Terrestrial
model
models,
carbon
Ecosystem
(LPJ)
High
as
exchange
Resolution
reported
Model
(NEE)
by
Standard deviation of net ecosystem carbon exchange (NEE)
estimated with three ecosystem models, High Resolution
Biosphere Model (HRBM), Terrestrial Ecosystem Model
(TEM), and Lund-Potsdam-Jena model (LPJ) as reported by
McGuire et al. [24].
of 300 Tg of C or more. Correspondingly, recent results
suggest that biomass burning is the largest source of interannual variability in land-atmosphere carbon fluxes [42].
Unlike respiration, fires return carbon to the atmosphere
as a wide range of compounds, some of which are chemically or radiatively active (e.g. methane, carbon monoxide
and aerosols), or are precursors to radiatively active gases
(e.g. ozone precursors). Methane and other hydrocarbons, carbon monoxide, and black carbon releases in
Africa are almost entirely of pyrogenic origin, and are thus
included in the biomass burning term (Table 1)
[27,28,41]. Methane consumption in upland soils is
small, and available estimates of methane release from
African wetlands suggest that they are globally insignificant [43]. However, given that there is no reliable map of
wetland extent in Africa, and virtually no direct emission
estimates, the true size of this flux is unknown. Recent
work suggests the possibility of a large methane source of
unknown magnitude from living plants [44,45]. Emissions of volatile organic compounds (VOCs) such as isoprene and monoterpenes have been studied in some
detail in southern and central Africa and are estimated to
return as much as 0.08 Pg C y-1 to the atmosphere [46]. At
the scale of the continent, industrial emissions of carbon
dioxide, carbon monoxide and hydrocarbons from Africa
are small, but can be locally very significant in the industrial areas of South Africa, the oilfields of the Gulf of
Guinea, Angola, and Libya, and around major cities elsewhere in Africa.
The export of dissolved organic and inorganic carbon
(DOC and DIC) in river water discharged to oceans is, by
and large, offset by DOC and DIC delivered in precipita-
Atmospheric mixing ratios and isotopic compositions
measured around the globe [48] can be used to estimate
terrestrial and oceanic carbon sources and sinks by inversion with atmospheric transport models [18,49-51].
Inverse solutions for Africa are poorly constrained due to
the lack of tropical, especially African, observations (Figure 4) [17,18]. This contributes to larger uncertainty
ranges around net CO2 flux estimates for Africa than for
global or tropical land areas, in general. Taken together,
inversion results demonstrate that Africa's net role in global carbon cycling is highly uncertain. Furthermore, lack
of data causes the inverse solution for southern Africa to
trade off with solutions for South America and the southern oceans, such that results can vary widely between
regions with no net change in overall source/sink strength
[e.g. [17,19]]. Solving this problem will require the addition of precise, long-term observations of carbon dioxide
in the tropics, located such that they help resolve the longitudinal differences among the southern hemisphere
regions [52-54]. Tropical atmospheric dynamics present
an additional challenge [55], and a source of uncertainty
that is not represented in Figure 4, because deep convection is both poorly represented in transport models and
poorly sampled, introducing non-negligible biases in
atmospheric inversions.
Recognizing large uncertainties in the inverse solutions,
inverse studies to date suggest that Africa as a whole is
approximately carbon neutral on an annual to long-term
basis. This is so despite significant carbon emissions
related to land use change and burning, implying that net
plant growth and corresponding sequestration of carbon
in vegetation and soils is sufficiently large across the continent to offset the loss terms. If inverse solutions correctly
estimate a carbon neutral Africa and assuming a neutral
biosphere with a background balance between net primary production and heterotrophic respiration plus natural fires, the remaining biotic uptake or sequestration can
be estimated as roughly offsetting Africa's land use (0.4 Pg
C y-1) plus fossil fuel (0.2 Pg C yr-1) sources, still noting
the large uncertainties.
Despite a near-zero balance, recent time-dependent
inverse solutions attribute much of the interannual variability (IAV) in global carbon sources/sinks to the African
continent [20,42,51,56]. Estimates of regional IAV are less
sensitive to transport and station-selection than seasonal
and long-term mean fluxes [20]. Global solutions for the
IAV of carbon sources/sinks [20] robustly indicate the
strong influence of global lands, particularly those in the
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3.5
Global
Land
-1
Net CO 2 flux [Pg C y ]
2.5
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Tropical
Land
S. Africa
0.5
-0.5
-1.5
B99, C00, R03, G02, G04
85-95, 85-95, 96-99, 92-96, 92-96
a
-3.5
100
80
Global
Land
Tropical
Land
Africa
N. Africa
S. Africa
60
-2
-1
N. Africa
1.5
-2.5
Net CO 2 flux [g C m y ]
Africa
40
20
0
-20
-40
-60
-80
b
-100
Figure 4 carbon source and sink estimates for Africa, tropical and global lands
Terrestrial
Terrestrial carbon source and sink estimates for Africa, tropical and global lands. (a) Net carbon dioxide flux totals, and (b) net
carbon dioxide flux per unit area. Positive values indicate a surface source. Boxes show the range of +/- 1 standard deviation
from the IPCC report [6] for global and tropical land during the 1980s (dark) and 1990s (light), whereas symbols report results
from inverse analyses cited in Appendix I. Triangles and error bars indicate mean flux estimates from individual inversion studies and associated posterior uncertainties. Squares indicate the average, and pluses indicate the standard deviation, of mean flux
estimates from a group of inverse solutions. Circles indicate the average uncertainty estimates among the group of inverse
solutions. Atmospheric inversion results for Africa are taken from Bousquet [18] (B99), Ciais [107] (C00), Rödenbeck [51]
(R03), Gurney [17] (G02), and Gurney [19] (G04), with years spanned in each analysis shown below literature source abbreviations of (a).
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tropics, with approximately equal contributions by lands
of tropical Asia, Africa, and southern and tropical America
(each about 0.5 Pg C y-1) (Figure 5). However, temporal
source/sink dynamics are still poorly constrained among
tropical regions, especially those of Africa and America
[20].
of carbon stocks and fluxes within and between the major
pools) is needed to advance process-level characterization
of seasonal and interannual variations in source/sink
strengths. Such data will help to improve the way biophysical and biogeochemical models represent African
ecosystems so that they capture the full suite of uniquely
African features such as the continent's seasonal fire
cycles, pastoralism, fuelwood harvest, cereal/grain production, dryland degradation, and the productivity and
isotopic signatures of its extensive C4 vegetation. In particular, carbon flux observations wherever existing need to
be used in model development and testing to appropriately represent the sensitivity of production and respiration processes to climate fluctuations. New collaborative
research programs and networks are emerging in Africa to
address some of the gaps through expanded site-based
and regional field measurements and model-based analyses (including, amongst many national and regional activities, the growing network of eddy covariance sites (the
"Afriflux" network), the African Monsoon Multidisciplinary Analysis (AMMA), the South African Ecological
Observation Network (SAEON), and the Environmental
Long-Term Observatories of southern Africa (ELTOSA)).
Taken together, large temporal variability of carbon
sources and sinks may be Africa's most significant contribution to the global carbon cycle. This is consistent with
results from ecosystem models [22-24], which indicate
that high interannual variability in rainfall throughout the
Sudano-Sahelian and southern African regions [57,58],
partly associated with the North Atlantic Oscillation, El
Niño Southern Oscillation, and South Pacific circulation
[59,60], introduce pronounced multi-year fluctuations in
surface-atmosphere C exchanges, which, in turn, appear
in atmospheric CO2 concentrations [51]. Inter-annual variability in NPP then translate to variability in fire emissions with a lag of several months to a year. Such
departures from long-term average biosphere exchange
[51] and fire emissions [42,51] may both be as large as the
net exchanges themselves.
Support for inventory and monitoring of soil and vegetation carbon stocks by forest and agricultural research stations, long-established in most African countries [61], is
critical. The associated national resource inventories [62]
provide information invaluable for assessing regionallyspecific ecosystem responses to natural and human disturbances and for anchoring regional-scale estimates of land
use related carbon sources and sinks. Synthesis of countrylevel data is of great importance to provide local and
regional scale data to underpin regional- and global-scale
Asia
Tropical Lands
Africa
SH
Tropical
0.0
NH
0.4
Lands Only
Oceans
0.8
Lands
Global
1.2
Land + Ocean
Root Mean Square of
Annual Net Carbon Flux
[Pg C y-1]
Given the need to better understand the spatial and temporal dimensions of the global carbon cycle for prediction
and management of future atmospheric CO2 concentrations, a number of research priorities for Africa emerge
from this review. Of primary importance is the need for
continent-wide observations that support both bottomup and top-down methods of estimating carbon sources
and sinks. Continued and new investment in collection
and synthesis of carbon cycle information (measurements
Amer.
The Future of Carbon Cycle Research in Africa
Figure
Root
and
for
mean
regions
5 square
based
of annual
on TransCom
net carbon
[20,flux
108]
obtained from time-dependent inverse solutions [20] for the period 1990 – 2001
Root mean square of annual net carbon flux obtained from time-dependent inverse solutions [20] for the period 1990 – 2001
and for regions based on TransCom [20, 108]. NH (Northern Hemisphere land) includes temperate and boreal Asia, temperate and boreal North America, and Europe, SH (Southern Hemisphere land) includes temperate South America, Australia and
New Zealand, Tropical includes tropical America, Africa and tropical Asia, and Amer. abbreviates America.
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carbon cycle assessments. These will complement developments in satellite remote sensing of vegetation biomass
using, for example, passive and active optical and radar
approaches [63,64].
fuels [6]. Non-industrialized countries currently contribute little to these emissions, but are vulnerable to climate
change and will therefore be forced to take potentially
costly measures to adapt.
A well-located atmospheric sampling network in Africa is
also needed to better constrain inversion estimates of
regional carbon sources and sinks and their temporal variability both in Africa and globally. However, improved
constraint relies not only on new observations but also
improvement of modeled transport and inverse estimation techniques. New transport schemes are needed to
represent deep tropical convection, while new data assimilation and computational techniques promise to better
resolve the African signal in global atmospheric carbon
dynamics by incorporating diurnal variation in surface
fluxes, multiple atmospheric tracers, and prior estimates
of fire emissions. With regard to additional carbon cycle
tracers, Africa is unique in having vast coverage of C4 vegetation [65], associated with a prevalence of semi-arid and
hot environments. Seasonal production, respiration, and
burning of C4 vegetation alter the carbon stable isotope
(13C:12C) composition of the atmosphere because C4
plants discriminate against the heavier isotope less than
C3 plants. This imprint provides a tracer for diagnosing
Africa's role in global carbon stocks and fluxes [40], presenting a potential opportunity for separation of moist
tropical forest exchange from that of the savanna regions.
Furthermore, since oceanic and C4 plant discrimination
are similar, information on the C4 terrestrial exchange is
critical for separation of terrestrial from oceanic fluxes.
Nearly all African countries are signatories to the UN
Framework Convention on Climate Change (UNFCCC)
and, being non-Annex 1 countries, there is no cap on their
greenhouse gas emissions in the first Kyoto Protocol commitment period. Still, parties in Africa can participate in
the Clean Development Mechanism (CDM) of the Kyoto
Protocol, under which developed countries that have
accepted emission caps are authorized to implement
projects that reduce emissions or sequester carbon in
developing countries. The resulting certified emissions
reductions can then be used to meet a fraction of Annex 1
emission targets. This mechanism provides opportunities
for less developed countries to leap-frog to clean industries using foreign investment and technology.
An orbiting space-based total column carbon observatory
covering the entire globe is anticipated within the next
decade [66], but it will still require near-surface and vertical profile measurements of CO2 for calibrating, validating, interpolating and interpreting satellite-derived
observations. Satellite-based assessments of local to
regional vegetation change from land use practices [e.g.
[67]] should be further explored for continental-scale
assessment. These and other data could be used to
develop land use/land cover transition models that represent Africa's unique human-vegetation-climate settings.
Such comprehensive investigations into regionally-specific ecosystem responses to land use in Africa offer
needed detail for representing the complex dynamics
associated with human-induced disturbances and land
use management.
Africa and the Climate Change Context
Recent International Panel on Climate Change (IPCC)
assessments show that industrialized nations are imposing a heavy burden of climate change on the global environment through emissions of carbon dioxide (CO2) and
other greenhouse gases, largely from the burning of fossil
The predominantly agricultural nations of Africa are
poorly-positioned to benefit financially and technologically from emission mitigation trading schemes, insofar as
these mechanisms focus mainly on industrial emissions
reductions, which are more easily verified. However, the
scope for carbon sequestration through management of
land in developing countries is large, and CDM provisions
for land use based carbon emission reductions might provide rapid, medium-term sequestration at relatively low
cost. Uncertainties surrounding the quantification and
verification of carbon sequestration through changes in
land management have thus far prevented large-scale
investment in this strategy. This situation could change
with improved understanding of carbon cycle dynamics
in terrestrial ecosystems and suitable verification schemes,
enabling many African nations to more easily participate
in global efforts to slow the rate of increase of atmospheric
CO2, as well as benefit from the financial and technological transfers.
Carbon sequestration through reforestation of lands
deforested prior to 1990 appears to be one of the most
readily available opportunities for a number of African
countries. Fire management presents another prospective
opportunity for mitigation, but reducing fire occurrence
has proven difficult in the past [e.g. [68]], and such programs would need to be wary of unintentional loss of biodiversity from fire-adapted biota. Climate change
mitigation through land management could also impart
unintended environmental and social costs that affect the
most vulnerable sectors of society, for example from converting lands in subsistence farming to large scale carbon
plantations, or by restriction of fuel wood harvest for
domestic uses. Such programs therefore require careful
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Carbon Balance and Management 2007, 2:3
evaluation of the potential costs and benefits, particularly
for already marginalized populations.
A Global Outlook
With as much as 40% of the world's fire emissions, about
20% of global net primary production and heterotrophic
respiration, at least 20% of global land use emissions, and
a major source of interannual variability in global net carbon exchange, African carbon dynamics are of global significance. The continent's vast carbon stocks seem to be
highly vulnerable to climate change, evidenced by strong
sensitivity of net ecosystem productivity and fire emissions to climate fluctuations. Being highly variable and
insufficiently studied, there is a need for continued and
enhanced observations of Africa's carbon stocks, fluxes,
and atmospheric concentrations to enable more precise
assessments of Africa's carbon cycle, and its sensitivity to
natural and anthropogenic pressures and future climate.
In years ahead, Africa's land use pressures will undoubtedly increase and climate changes are anticipated to intensify drought cycles and make much of Africa warmer and
dryer [69]. Furthermore, increasing exploitation of forest
resource in the moist tropics is anticipated with economic
development and investment in logging infrastructure.
Such changes will likely release CO2 to the atmosphere as
well as increase the magnitude of interannual variation in
Africa's C fluxes by increasing Africa's biomass burning
emissions and reducing the continent's net ecosystem
productivity. If realized, these trends would have enormously important implications for global carbon dynamics and biospheric feedbacks to the climate system.
Competing interests
The author(s) declare that they have no competing interests.
http://www.cbmjournal.com/content/2/1/3
mean estimate was reported in one of the many sources,
we attempted to incorporate some of this uncertainty in
the tabular values of Table 1 by including the mean, mean
plus standard deviation, and mean minus standard deviation all as independent estimates contributing to the sample.
Annual precipitation C flux to Africa was estimated from
the sum of estimates for dissolved organic and inorganic
(DOC and DIC) carbon fluxes from precipitation following the approach in Kempe [70]. For DOC, the flux was
calculated as the product of annual precipitation water
flux with the maximum or minimum observed continental rainwater DOC reported in Willey et al. [71], where
precipitation delivered to Africa was estimated from an
FAO rainfall product [72]. Similarly, the DIC flux was calculated as the product of annual African precipitation
with a) continental rainwater DIC at a pH of 7.4 and 10°C
as in Willey et al. [71], and b) its product with the mean
CO2 content of precipitation reported in Miotke [73].
Africa's annual riverine C discharge to oceans was calculated from the sum of riverine DOC and DIC flux estimates also as in Kempe [70]. For DIC the flux was
calculated as the product of Africa's riverine discharge
[74,75] with DIC content of Africa's river water [74]. For
DOC, we used the global ratio of DOC to DIC in river
water [74] to estimate DOC content of Africa's river water,
which was then multiplied by river water discharge.
When not directly reported, carbon emissions from
human-managed fires were estimated by converting biomass burned into carbon emissions based on a common
[e.g. [27,28,76]] assumption of biomass to carbon emissions ratio of ~0.45.
Acknowledgements
Authors' contributions
All authors participated in detailed discussions that led to
this review paper. CAW compiled and analyzed the data
and drafted the manuscript. NPH originally conceived the
paper and contributed to data analyses, interpretation,
drafting and editing the manuscript. JCN, RJS, JAB and
ASD provided intellectual input on available data and previous analyses, and on the synthesis, presentation and
interpretation needed for this review. DFB made data
available from a global time-dependent inverse analysis of
CO2 concentrations contributing to Figure 5. All of the
authors read, edited, and approved the final manuscript.
Appendix 1. Methods
Table 1 contains statistics of a sample of independent
mean estimates for each term, presenting arithmetic
means of reported values, their maxima and minima, and
standard deviations. When a standard deviation around a
Funding for this study was provided by the United States National Aeronautics and Space Administration (NASA) Terrestrial Ecology Program (Dr.
Diane Wickland), and the National Oceanic and Atmospheric Administration (NOAA) Global Carbon Cycle Program (Dr. Kathy Tedesco). We
would like to thank A.J. Dolman of Vrije University, Amsterdam, and Robert B. Jackson of Duke University for providing initial reviews of the draft
manuscript.
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